Overview

Dataset statistics

Number of variables17
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory149.2 KiB
Average record size in memory763.7 B

Variable types

Numeric9
DateTime1
Categorical7

Alerts

id_registo is uniformly distributedUniform
id_registo has unique valuesUnique
erros_tecnicos has 11 (5.5%) zerosZeros

Reproduction

Analysis started2026-02-17 17:30:06.219528
Analysis finished2026-02-17 17:30:10.015399
Duration3.8 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

id_registo
Real number (ℝ)

Uniform  Unique 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.5
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-02-17T17:30:10.061532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.95
Q150.75
median100.5
Q3150.25
95-th percentile190.05
Maximum200
Range199
Interquartile range (IQR)99.5

Descriptive statistics

Standard deviation57.879185
Coefficient of variation (CV)0.57591228
Kurtosis-1.2
Mean100.5
Median Absolute Deviation (MAD)50
Skewness0
Sum20100
Variance3350
MonotonicityStrictly increasing
2026-02-17T17:30:10.115358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.5%
21
 
0.5%
31
 
0.5%
41
 
0.5%
51
 
0.5%
61
 
0.5%
71
 
0.5%
81
 
0.5%
91
 
0.5%
101
 
0.5%
Other values (190)190
95.0%
ValueCountFrequency (%)
11
0.5%
21
0.5%
31
0.5%
41
0.5%
51
0.5%
61
0.5%
71
0.5%
81
0.5%
91
0.5%
101
0.5%
ValueCountFrequency (%)
2001
0.5%
1991
0.5%
1981
0.5%
1971
0.5%
1961
0.5%
1951
0.5%
1941
0.5%
1931
0.5%
1921
0.5%
1911
0.5%
Distinct152
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2024-01-02 00:00:00
Maximum2024-12-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-17T17:30:10.165515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:10.218466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size27.7 KiB
Serviços De Educação
38 
Direcção De Recursos Humanos
32 
Departamento De Inovação E Digitalização
30 
Gabinete De Atendimento Ao Cidadão
28 
Serviços De Ti
26 
Other values (3)
46 

Length

Max length40
Median length33
Mean length28.52
Min length14

Characters and Unicode

Total characters5704
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirecção De Serviços Financeiros
2nd rowDepartamento De Inovação E Digitalização
3rd rowDirecção De Recursos Humanos
4th rowDepartamento De Planeamento Estratégico
5th rowDirecção De Recursos Humanos

Common Values

ValueCountFrequency (%)
Serviços De Educação38
19.0%
Direcção De Recursos Humanos32
16.0%
Departamento De Inovação E Digitalização30
15.0%
Gabinete De Atendimento Ao Cidadão28
14.0%
Serviços De Ti26
13.0%
Direcção De Serviços Financeiros16
8.0%
Departamento De Planeamento Estratégico15
 
7.5%
Departamento De Saúde Pública15
 
7.5%

Length

2026-02-17T17:30:10.268502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T17:30:10.305120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
de200
25.2%
serviços80
 
10.1%
departamento60
 
7.6%
direcção48
 
6.0%
educação38
 
4.8%
recursos32
 
4.0%
humanos32
 
4.0%
inovação30
 
3.8%
e30
 
3.8%
digitalização30
 
3.8%
Other values (10)214
27.0%

Most occurring characters

ValueCountFrequency (%)
e653
 
11.4%
594
 
10.4%
o510
 
8.9%
a427
 
7.5%
i390
 
6.8%
D338
 
5.9%
t279
 
4.9%
n268
 
4.7%
r251
 
4.4%
ç226
 
4.0%
Other values (25)1768
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e653
 
11.4%
594
 
10.4%
o510
 
8.9%
a427
 
7.5%
i390
 
6.8%
D338
 
5.9%
t279
 
4.9%
n268
 
4.7%
r251
 
4.4%
ç226
 
4.0%
Other values (25)1768
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e653
 
11.4%
594
 
10.4%
o510
 
8.9%
a427
 
7.5%
i390
 
6.8%
D338
 
5.9%
t279
 
4.9%
n268
 
4.7%
r251
 
4.4%
ç226
 
4.0%
Other values (25)1768
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e653
 
11.4%
594
 
10.4%
o510
 
8.9%
a427
 
7.5%
i390
 
6.8%
D338
 
5.9%
t279
 
4.9%
n268
 
4.7%
r251
 
4.4%
ç226
 
4.0%
Other values (25)1768
31.0%

tipo_servico
Categorical

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
Portal Online
36 
Serviços Digitais
32 
Telefone
32 
Email
29 
Balcão Único
29 
Other values (2)
42 

Length

Max length22
Median length12.5
Mean length12.15
Min length5

Characters and Unicode

Total characters2430
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTelefone
2nd rowBalcão Único
3rd rowChatbot
4th rowEmail
5th rowAtendimento Presencial

Common Values

ValueCountFrequency (%)
Portal Online36
18.0%
Serviços Digitais32
16.0%
Telefone32
16.0%
Email29
14.5%
Balcão Único29
14.5%
Atendimento Presencial25
12.5%
Chatbot17
8.5%

Length

2026-02-17T17:30:10.369672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T17:30:10.405208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
portal36
11.2%
online36
11.2%
serviços32
9.9%
digitais32
9.9%
telefone32
9.9%
email29
9.0%
balcão29
9.0%
único29
9.0%
atendimento25
7.8%
presencial25
7.8%

Most occurring characters

ValueCountFrequency (%)
i272
11.2%
e264
 
10.9%
n208
 
8.6%
o200
 
8.2%
l187
 
7.7%
a168
 
6.9%
t152
 
6.3%
122
 
5.0%
r93
 
3.8%
s89
 
3.7%
Other values (20)675
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)2430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i272
11.2%
e264
 
10.9%
n208
 
8.6%
o200
 
8.2%
l187
 
7.7%
a168
 
6.9%
t152
 
6.3%
122
 
5.0%
r93
 
3.8%
s89
 
3.7%
Other values (20)675
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i272
11.2%
e264
 
10.9%
n208
 
8.6%
o200
 
8.2%
l187
 
7.7%
a168
 
6.9%
t152
 
6.3%
122
 
5.0%
r93
 
3.8%
s89
 
3.7%
Other values (20)675
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i272
11.2%
e264
 
10.9%
n208
 
8.6%
o200
 
8.2%
l187
 
7.7%
a168
 
6.9%
t152
 
6.3%
122
 
5.0%
r93
 
3.8%
s89
 
3.7%
Other values (20)675
27.8%

indicador_si
Real number (ℝ)

Distinct194
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.58325
Minimum60.43
Maximum99.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-02-17T17:30:10.461440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60.43
5-th percentile62.2285
Q171.3925
median82.39
Q392.5575
95-th percentile98.8
Maximum99.99
Range39.56
Interquartile range (IQR)21.165

Descriptive statistics

Standard deviation12.373367
Coefficient of variation (CV)0.15166553
Kurtosis-1.2537261
Mean81.58325
Median Absolute Deviation (MAD)10.495
Skewness-0.19602681
Sum16316.65
Variance153.10021
MonotonicityNot monotonic
2026-02-17T17:30:10.515003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.022
 
1.0%
63.652
 
1.0%
80.812
 
1.0%
98.82
 
1.0%
78.752
 
1.0%
84.732
 
1.0%
75.531
 
0.5%
83.021
 
0.5%
85.731
 
0.5%
78.331
 
0.5%
Other values (184)184
92.0%
ValueCountFrequency (%)
60.431
0.5%
60.451
0.5%
60.491
0.5%
60.731
0.5%
60.931
0.5%
61.051
0.5%
61.151
0.5%
61.731
0.5%
61.811
0.5%
61.821
0.5%
ValueCountFrequency (%)
99.991
0.5%
99.871
0.5%
99.721
0.5%
99.621
0.5%
99.491
0.5%
99.471
0.5%
99.451
0.5%
99.441
0.5%
98.981
0.5%
98.82
1.0%

taxa_resolucao
Real number (ℝ)

Distinct195
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.90985
Minimum70.41
Maximum97.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-02-17T17:30:10.561643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum70.41
5-th percentile72.2435
Q175.84
median82.275
Q388.7925
95-th percentile96.0205
Maximum97.72
Range27.31
Interquartile range (IQR)12.9525

Descriptive statistics

Standard deviation7.7699499
Coefficient of variation (CV)0.093715643
Kurtosis-1.1095694
Mean82.90985
Median Absolute Deviation (MAD)6.47
Skewness0.26841703
Sum16581.97
Variance60.372122
MonotonicityNot monotonic
2026-02-17T17:30:10.608499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.582
 
1.0%
95.112
 
1.0%
90.932
 
1.0%
76.792
 
1.0%
84.912
 
1.0%
75.961
 
0.5%
70.411
 
0.5%
80.981
 
0.5%
89.711
 
0.5%
73.821
 
0.5%
Other values (185)185
92.5%
ValueCountFrequency (%)
70.411
0.5%
70.561
0.5%
71.031
0.5%
71.11
0.5%
71.141
0.5%
71.451
0.5%
71.711
0.5%
71.771
0.5%
71.861
0.5%
72.121
0.5%
ValueCountFrequency (%)
97.721
0.5%
97.621
0.5%
97.511
0.5%
97.441
0.5%
97.151
0.5%
97.021
0.5%
96.591
0.5%
96.321
0.5%
96.271
0.5%
96.031
0.5%

tempo_resposta
Real number (ℝ)

Distinct99
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.73
Minimum1
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-02-17T17:30:10.660827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q129.75
median54.5
Q388.25
95-th percentile115
Maximum118
Range117
Interquartile range (IQR)58.5

Descriptive statistics

Standard deviation33.991088
Coefficient of variation (CV)0.57876874
Kurtosis-1.1278132
Mean58.73
Median Absolute Deviation (MAD)28
Skewness0.1315968
Sum11746
Variance1155.3941
MonotonicityNot monotonic
2026-02-17T17:30:10.710473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
296
 
3.0%
475
 
2.5%
495
 
2.5%
384
 
2.0%
1184
 
2.0%
1154
 
2.0%
634
 
2.0%
904
 
2.0%
14
 
2.0%
1044
 
2.0%
Other values (89)156
78.0%
ValueCountFrequency (%)
14
2.0%
22
1.0%
53
1.5%
61
 
0.5%
71
 
0.5%
81
 
0.5%
92
1.0%
101
 
0.5%
112
1.0%
121
 
0.5%
ValueCountFrequency (%)
1184
2.0%
1172
1.0%
1161
 
0.5%
1154
2.0%
1142
1.0%
1132
1.0%
1121
 
0.5%
1111
 
0.5%
1101
 
0.5%
1091
 
0.5%

satisfacao_cidadao
Real number (ℝ)

Distinct21
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9855
Minimum3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-02-17T17:30:10.751604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.1
Q13.5
median4
Q34.5
95-th percentile4.9
Maximum5
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5910894
Coefficient of variation (CV)0.14830997
Kurtosis-1.2681659
Mean3.9855
Median Absolute Deviation (MAD)0.5
Skewness-0.00039139624
Sum797.1
Variance0.34938668
MonotonicityNot monotonic
2026-02-17T17:30:10.794078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3.517
 
8.5%
4.715
 
7.5%
3.412
 
6.0%
4.312
 
6.0%
4.212
 
6.0%
3.811
 
5.5%
4.110
 
5.0%
3.210
 
5.0%
4.410
 
5.0%
4.810
 
5.0%
Other values (11)81
40.5%
ValueCountFrequency (%)
37
3.5%
3.110
5.0%
3.210
5.0%
3.38
4.0%
3.412
6.0%
3.517
8.5%
3.68
4.0%
3.77
3.5%
3.811
5.5%
3.95
 
2.5%
ValueCountFrequency (%)
54
 
2.0%
4.99
4.5%
4.810
5.0%
4.715
7.5%
4.68
4.0%
4.58
4.0%
4.410
5.0%
4.312
6.0%
4.212
6.0%
4.110
5.0%

volume_interacoes
Real number (ℝ)

Distinct162
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251.59
Minimum12
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-02-17T17:30:10.841469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile37.8
Q1131.25
median251
Q3366.5
95-th percentile470.1
Maximum499
Range487
Interquartile range (IQR)235.25

Descriptive statistics

Standard deviation142.12678
Coefficient of variation (CV)0.56491427
Kurtosis-1.21564
Mean251.59
Median Absolute Deviation (MAD)118.5
Skewness-0.02290788
Sum50318
Variance20200.022
MonotonicityNot monotonic
2026-02-17T17:30:10.892254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
494
 
2.0%
2354
 
2.0%
683
 
1.5%
4703
 
1.5%
4723
 
1.5%
1862
 
1.0%
702
 
1.0%
3532
 
1.0%
3852
 
1.0%
122
 
1.0%
Other values (152)173
86.5%
ValueCountFrequency (%)
122
1.0%
141
0.5%
161
0.5%
182
1.0%
201
0.5%
291
0.5%
321
0.5%
341
0.5%
381
0.5%
401
0.5%
ValueCountFrequency (%)
4991
 
0.5%
4951
 
0.5%
4941
 
0.5%
4871
 
0.5%
4861
 
0.5%
4851
 
0.5%
4811
 
0.5%
4723
1.5%
4703
1.5%
4631
 
0.5%

canal_utilizado
Categorical

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
Presencial
38 
Aplicacao_Movel
37 
Chatbot
35 
Email
32 
Portal
30 

Length

Max length15
Median length8
Mean length8.72
Min length5

Characters and Unicode

Total characters1744
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPresencial
2nd rowEmail
3rd rowTelefone
4th rowAplicacao_Movel
5th rowPortal

Common Values

ValueCountFrequency (%)
Presencial38
19.0%
Aplicacao_Movel37
18.5%
Chatbot35
17.5%
Email32
16.0%
Portal30
15.0%
Telefone28
14.0%

Length

2026-02-17T17:30:10.939630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T17:30:10.971470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
presencial38
19.0%
aplicacao_movel37
18.5%
chatbot35
17.5%
email32
16.0%
portal30
15.0%
telefone28
14.0%

Most occurring characters

ValueCountFrequency (%)
a209
12.0%
l202
11.6%
e197
11.3%
o167
 
9.6%
c112
 
6.4%
i107
 
6.1%
t100
 
5.7%
P68
 
3.9%
r68
 
3.9%
n66
 
3.8%
Other values (13)448
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1744
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a209
12.0%
l202
11.6%
e197
11.3%
o167
 
9.6%
c112
 
6.4%
i107
 
6.1%
t100
 
5.7%
P68
 
3.9%
r68
 
3.9%
n66
 
3.8%
Other values (13)448
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1744
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a209
12.0%
l202
11.6%
e197
11.3%
o167
 
9.6%
c112
 
6.4%
i107
 
6.1%
t100
 
5.7%
P68
 
3.9%
r68
 
3.9%
n66
 
3.8%
Other values (13)448
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1744
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a209
12.0%
l202
11.6%
e197
11.3%
o167
 
9.6%
c112
 
6.4%
i107
 
6.1%
t100
 
5.7%
P68
 
3.9%
r68
 
3.9%
n66
 
3.8%
Other values (13)448
25.7%

taxa_abandono
Real number (ℝ)

Distinct194
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5555
Minimum0.23
Maximum24.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-02-17T17:30:11.024157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile1.708
Q17.8975
median13.325
Q316.595
95-th percentile23.501
Maximum24.83
Range24.6
Interquartile range (IQR)8.6975

Descriptive statistics

Standard deviation6.5683697
Coefficient of variation (CV)0.52314681
Kurtosis-0.86492272
Mean12.5555
Median Absolute Deviation (MAD)4.97
Skewness-0.054645417
Sum2511.1
Variance43.143481
MonotonicityNot monotonic
2026-02-17T17:30:11.078475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.262
 
1.0%
14.942
 
1.0%
8.982
 
1.0%
13.562
 
1.0%
14.72
 
1.0%
10.132
 
1.0%
14.871
 
0.5%
23.681
 
0.5%
7.471
 
0.5%
22.411
 
0.5%
Other values (184)184
92.0%
ValueCountFrequency (%)
0.231
0.5%
0.371
0.5%
0.431
0.5%
0.441
0.5%
0.521
0.5%
0.731
0.5%
0.851
0.5%
1.021
0.5%
1.091
0.5%
1.291
0.5%
ValueCountFrequency (%)
24.831
0.5%
24.81
0.5%
24.671
0.5%
24.471
0.5%
24.331
0.5%
24.081
0.5%
24.021
0.5%
23.711
0.5%
23.681
0.5%
23.521
0.5%

erros_tecnicos
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.035
Minimum0
Maximum14
Zeros11
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-02-17T17:30:11.116634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q311
95-th percentile14
Maximum14
Range14
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.1762399
Coefficient of variation (CV)0.59363752
Kurtosis-1.1392312
Mean7.035
Median Absolute Deviation (MAD)4
Skewness0.0022691776
Sum1407
Variance17.44098
MonotonicityNot monotonic
2026-02-17T17:30:11.152460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
918
 
9.0%
617
 
8.5%
317
 
8.5%
1216
 
8.0%
414
 
7.0%
1113
 
6.5%
1413
 
6.5%
813
 
6.5%
112
 
6.0%
512
 
6.0%
Other values (5)55
27.5%
ValueCountFrequency (%)
011
5.5%
112
6.0%
211
5.5%
317
8.5%
414
7.0%
512
6.0%
617
8.5%
712
6.0%
813
6.5%
918
9.0%
ValueCountFrequency (%)
1413
6.5%
139
4.5%
1216
8.0%
1113
6.5%
1012
6.0%
918
9.0%
813
6.5%
712
6.0%
617
8.5%
512
6.0%

transparencia
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
Sim
149 
Não
51 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters600
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSim
2nd rowSim
3rd rowNão
4th rowSim
5th rowSim

Common Values

ValueCountFrequency (%)
Sim149
74.5%
Não51
 
25.5%

Length

2026-02-17T17:30:11.194093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T17:30:11.219172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sim149
74.5%
não51
 
25.5%

Most occurring characters

ValueCountFrequency (%)
S149
24.8%
i149
24.8%
m149
24.8%
N51
 
8.5%
ã51
 
8.5%
o51
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S149
24.8%
i149
24.8%
m149
24.8%
N51
 
8.5%
ã51
 
8.5%
o51
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S149
24.8%
i149
24.8%
m149
24.8%
N51
 
8.5%
ã51
 
8.5%
o51
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S149
24.8%
i149
24.8%
m149
24.8%
N51
 
8.5%
ã51
 
8.5%
o51
 
8.5%

feedback_cidadao
Categorical

Distinct10
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size18.1 KiB
Sistema Lento
24 
Serviço Eficiente
22 
Precisa Melhorias
21 
Muito Satisfeito
21 
Falta De Informação
21 
Other values (5)
91 

Length

Max length24
Median length21
Mean length17.76
Min length11

Characters and Unicode

Total characters3552
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProcesso Complicado
2nd rowBom Serviço
3rd rowProcesso Complicado
4th rowProcesso Complicado
5th rowServiço Eficiente

Common Values

ValueCountFrequency (%)
Sistema Lento24
12.0%
Serviço Eficiente22
11.0%
Precisa Melhorias21
10.5%
Muito Satisfeito21
10.5%
Falta De Informação21
10.5%
Tempo De Espera Elevado20
10.0%
Bom Serviço19
9.5%
Processo Complicado19
9.5%
Resposta Rápida E Eficaz17
8.5%
Excelente Atendimento16
8.0%

Length

2026-02-17T17:30:11.254392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T17:30:11.296414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
serviço41
 
8.3%
de41
 
8.3%
sistema24
 
4.8%
lento24
 
4.8%
eficiente22
 
4.4%
melhorias21
 
4.2%
precisa21
 
4.2%
muito21
 
4.2%
satisfeito21
 
4.2%
falta21
 
4.2%
Other values (13)238
48.1%

Most occurring characters

ValueCountFrequency (%)
e413
 
11.6%
o338
 
9.5%
295
 
8.3%
i283
 
8.0%
a260
 
7.3%
t219
 
6.2%
s179
 
5.0%
r143
 
4.0%
m119
 
3.4%
n115
 
3.2%
Other values (26)1188
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e413
 
11.6%
o338
 
9.5%
295
 
8.3%
i283
 
8.0%
a260
 
7.3%
t219
 
6.2%
s179
 
5.0%
r143
 
4.0%
m119
 
3.4%
n115
 
3.2%
Other values (26)1188
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e413
 
11.6%
o338
 
9.5%
295
 
8.3%
i283
 
8.0%
a260
 
7.3%
t219
 
6.2%
s179
 
5.0%
r143
 
4.0%
m119
 
3.4%
n115
 
3.2%
Other values (26)1188
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e413
 
11.6%
o338
 
9.5%
295
 
8.3%
i283
 
8.0%
a260
 
7.3%
t219
 
6.2%
s179
 
5.0%
r143
 
4.0%
m119
 
3.4%
n115
 
3.2%
Other values (26)1188
33.4%
Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size17.9 KiB
Empresa
37 
Utilizador Vulnerável
36 
Cidadão
34 
Estudante
32 
Reforma/Pensionista
32 

Length

Max length21
Median length19
Mean length13.5
Min length7

Characters and Unicode

Total characters2700
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmpresa
2nd rowFuncionário Público
3rd rowEstudante
4th rowFuncionário Público
5th rowCidadão

Common Values

ValueCountFrequency (%)
Empresa37
18.5%
Utilizador Vulnerável36
18.0%
Cidadão34
17.0%
Estudante32
16.0%
Reforma/Pensionista32
16.0%
Funcionário Público29
14.5%

Length

2026-02-17T17:30:11.361384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T17:30:11.394953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
empresa37
14.0%
utilizador36
13.6%
vulnerável36
13.6%
cidadão34
12.8%
estudante32
12.1%
reforma/pensionista32
12.1%
funcionário29
10.9%
público29
10.9%

Most occurring characters

ValueCountFrequency (%)
i257
 
9.5%
o221
 
8.2%
e205
 
7.6%
a203
 
7.5%
n190
 
7.0%
r170
 
6.3%
l137
 
5.1%
d136
 
5.0%
s133
 
4.9%
t132
 
4.9%
Other values (20)916
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i257
 
9.5%
o221
 
8.2%
e205
 
7.6%
a203
 
7.5%
n190
 
7.0%
r170
 
6.3%
l137
 
5.1%
d136
 
5.0%
s133
 
4.9%
t132
 
4.9%
Other values (20)916
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i257
 
9.5%
o221
 
8.2%
e205
 
7.6%
a203
 
7.5%
n190
 
7.0%
r170
 
6.3%
l137
 
5.1%
d136
 
5.0%
s133
 
4.9%
t132
 
4.9%
Other values (20)916
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i257
 
9.5%
o221
 
8.2%
e205
 
7.6%
a203
 
7.5%
n190
 
7.0%
r170
 
6.3%
l137
 
5.1%
d136
 
5.0%
s133
 
4.9%
t132
 
4.9%
Other values (20)916
33.9%

area_tematica
Categorical

Distinct9
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
Social
30 
Habitação
27 
Inovação
26 
Ambiente
22 
Digitalização
22 
Other values (4)
73 

Length

Max length13
Median length9
Mean length8.205
Min length5

Characters and Unicode

Total characters1641
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCultura
2nd rowSaúde
3rd rowInovação
4th rowDigitalização
5th rowSaúde

Common Values

ValueCountFrequency (%)
Social30
15.0%
Habitação27
13.5%
Inovação26
13.0%
Ambiente22
11.0%
Digitalização22
11.0%
Cultura21
10.5%
Mobilidade18
9.0%
Saúde17
8.5%
Educação17
8.5%

Length

2026-02-17T17:30:11.449488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-17T17:30:11.583091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
social30
15.0%
habitação27
13.5%
inovação26
13.0%
ambiente22
11.0%
digitalização22
11.0%
cultura21
10.5%
mobilidade18
9.0%
saúde17
8.5%
educação17
8.5%

Most occurring characters

ValueCountFrequency (%)
a227
13.8%
i181
 
11.0%
o166
 
10.1%
ã92
 
5.6%
t92
 
5.6%
ç92
 
5.6%
l91
 
5.5%
e79
 
4.8%
d70
 
4.3%
b67
 
4.1%
Other values (17)484
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1641
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a227
13.8%
i181
 
11.0%
o166
 
10.1%
ã92
 
5.6%
t92
 
5.6%
ç92
 
5.6%
l91
 
5.5%
e79
 
4.8%
d70
 
4.3%
b67
 
4.1%
Other values (17)484
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1641
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a227
13.8%
i181
 
11.0%
o166
 
10.1%
ã92
 
5.6%
t92
 
5.6%
ç92
 
5.6%
l91
 
5.5%
e79
 
4.8%
d70
 
4.3%
b67
 
4.1%
Other values (17)484
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1641
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a227
13.8%
i181
 
11.0%
o166
 
10.1%
ã92
 
5.6%
t92
 
5.6%
ç92
 
5.6%
l91
 
5.5%
e79
 
4.8%
d70
 
4.3%
b67
 
4.1%
Other values (17)484
29.5%

indicador_kpi
Real number (ℝ)

Distinct197
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.1668
Minimum50.11
Maximum99.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-02-17T17:30:11.649412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.11
5-th percentile52.239
Q163.075
median75.475
Q387.61
95-th percentile96.513
Maximum99.99
Range49.88
Interquartile range (IQR)24.535

Descriptive statistics

Standard deviation14.210823
Coefficient of variation (CV)0.18905718
Kurtosis-1.129439
Mean75.1668
Median Absolute Deviation (MAD)12.42
Skewness-0.051252587
Sum15033.36
Variance201.9475
MonotonicityNot monotonic
2026-02-17T17:30:11.704173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.262
 
1.0%
52.242
 
1.0%
90.842
 
1.0%
63.581
 
0.5%
73.641
 
0.5%
63.451
 
0.5%
99.191
 
0.5%
70.351
 
0.5%
94.571
 
0.5%
80.21
 
0.5%
Other values (187)187
93.5%
ValueCountFrequency (%)
50.111
0.5%
50.151
0.5%
50.341
0.5%
50.621
0.5%
51.371
0.5%
51.481
0.5%
51.551
0.5%
51.991
0.5%
52.031
0.5%
52.221
0.5%
ValueCountFrequency (%)
99.991
0.5%
99.741
0.5%
99.561
0.5%
99.191
0.5%
99.011
0.5%
98.871
0.5%
98.61
0.5%
98.541
0.5%
97.71
0.5%
97.331
0.5%

Interactions

2026-02-17T17:30:09.462985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:06.606425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:06.961468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.316315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.641607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.983939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.467304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.798957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.124091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.500673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:06.646618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.001633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.354485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.684344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.028587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.505283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.836550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.160917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.538012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:06.685423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.043023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.391492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.722374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.073280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.543065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.873978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.204044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.571678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:06.725076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.079489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.426646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.759265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.214091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.577153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.908494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.239269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.607416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:06.763029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.119596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.461317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.795065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.255190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.613297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.943002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.277574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.647534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:06.806424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.161763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.500041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.837640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.297471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.654525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.983001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.318660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.683073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:06.843097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.201687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.534049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.873245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.340603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.691697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.018615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.354264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.716245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:06.880489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.238260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.568409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.910634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.384019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.724967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.051669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.390483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.754910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:06.924217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.279316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.607837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:07.946676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.429638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:08.763368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.088471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-17T17:30:09.427302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-17T17:30:11.753343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
area_tematicacanal_utilizadoerros_tecnicosfeedback_cidadaoid_registoindicador_kpiindicador_sisatisfacao_cidadaosegmentacao_utilizadortaxa_abandonotaxa_resolucaotempo_respostatipo_servicotransparenciaunidade_organizacionalvolume_interacoes
area_tematica1.0000.0640.0000.0670.0000.0660.0000.0990.1110.0000.0660.0800.0000.0000.0540.000
canal_utilizado0.0641.0000.0000.0440.0000.0700.0000.0000.0660.0000.0000.0000.0000.0000.0940.000
erros_tecnicos0.0000.0001.0000.000-0.1410.000-0.009-0.1760.1610.0400.0390.0100.0000.0000.000-0.041
feedback_cidadao0.0670.0440.0001.0000.0000.0000.1200.0990.0480.0920.0980.0000.0340.1270.0360.063
id_registo0.0000.000-0.1410.0001.0000.0110.0800.1880.093-0.0150.1350.0700.0000.0000.065-0.091
indicador_kpi0.0660.0700.0000.0000.0111.0000.016-0.0930.017-0.0120.052-0.0030.0610.1810.000-0.153
indicador_si0.0000.000-0.0090.1200.0800.0161.0000.0740.161-0.1540.0250.0140.1400.0520.1310.008
satisfacao_cidadao0.0990.000-0.1760.0990.188-0.0930.0741.0000.0510.134-0.008-0.0390.1570.0000.000-0.030
segmentacao_utilizador0.1110.0660.1610.0480.0930.0170.1610.0511.0000.1220.0000.0540.0420.0000.0000.140
taxa_abandono0.0000.0000.0400.092-0.015-0.012-0.1540.1340.1221.000-0.033-0.0020.0000.0000.000-0.091
taxa_resolucao0.0660.0000.0390.0980.1350.0520.025-0.0080.000-0.0331.0000.0920.1020.0000.0650.063
tempo_resposta0.0800.0000.0100.0000.070-0.0030.014-0.0390.054-0.0020.0921.0000.0000.0000.0850.021
tipo_servico0.0000.0000.0000.0340.0000.0610.1400.1570.0420.0000.1020.0001.0000.1220.0470.000
transparencia0.0000.0000.0000.1270.0000.1810.0520.0000.0000.0000.0000.0000.1221.0000.1280.000
unidade_organizacional0.0540.0940.0000.0360.0650.0000.1310.0000.0000.0000.0650.0850.0470.1281.0000.032
volume_interacoes0.0000.000-0.0410.063-0.091-0.1530.008-0.0300.140-0.0910.0630.0210.0000.0000.0321.000

Missing values

2026-02-17T17:30:09.817491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-17T17:30:09.885034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

id_registodata_registounidade_organizacionaltipo_servicoindicador_sitaxa_resolucaotempo_respostasatisfacao_cidadaovolume_interacoescanal_utilizadotaxa_abandonoerros_tecnicostransparenciafeedback_cidadaosegmentacao_utilizadorarea_tematicaindicador_kpi
012024-04-12Direcção De Serviços FinanceirosTelefone83.0287.37363.8368Presencial22.417SimProcesso ComplicadoEmpresaCultura63.13
122024-12-14Departamento De Inovação E DigitalizaçãoBalcão Único75.5389.71294.1349Email14.871SimBom ServiçoFuncionário PúblicoSaúde63.58
232024-09-27Direcção De Recursos HumanosChatbot85.7375.96383.3398Telefone15.425NãoProcesso ComplicadoEstudanteInovação73.64
342024-04-16Departamento De Planeamento EstratégicoEmail78.3373.82573.474Aplicacao_Movel15.3211SimProcesso ComplicadoFuncionário PúblicoDigitalização99.19
452024-03-12Direcção De Recursos HumanosAtendimento Presencial81.8270.41974.7487Portal7.474SimServiço EficienteCidadãoSaúde63.45
562024-07-07Serviços De EducaçãoTelefone97.6679.821014.9186Chatbot23.3812NãoMuito SatisfeitoReforma/PensionistaMobilidade70.35
672024-01-21Direcção De Recursos HumanosTelefone75.4486.52273.712Chatbot23.689SimMuito SatisfeitoReforma/PensionistaSocial94.57
782024-04-12Departamento De Inovação E DigitalizaçãoAtendimento Presencial98.4580.98553.5438Chatbot15.638SimFalta De InformaçãoReforma/PensionistaSocial50.15
892024-05-01Direcção De Recursos HumanosBalcão Único96.2182.25334.3151Email19.5911SimPrecisa MelhoriasEstudanteSaúde51.99
9102024-08-02Direcção De Serviços FinanceirosTelefone67.8395.32683.8385Presencial15.314SimServiço EficienteReforma/PensionistaCultura80.20
id_registodata_registounidade_organizacionaltipo_servicoindicador_sitaxa_resolucaotempo_respostasatisfacao_cidadaovolume_interacoescanal_utilizadotaxa_abandonoerros_tecnicostransparenciafeedback_cidadaosegmentacao_utilizadorarea_tematicaindicador_kpi
1901912024-11-10Departamento De Inovação E DigitalizaçãoAtendimento Presencial90.8987.48185.0446Aplicacao_Movel14.912SimSistema LentoFuncionário PúblicoInovação89.63
1911922024-06-08Serviços De EducaçãoTelefone80.8173.2713.7167Email13.648SimSistema LentoUtilizador VulnerávelHabitação73.07
1921932024-04-05Direcção De Recursos HumanosTelefone94.0996.32784.548Email11.117SimFalta De InformaçãoEmpresaSocial86.54
1931942024-08-20Serviços De EducaçãoEmail82.0887.581153.8424Email0.370SimBom ServiçoUtilizador VulnerávelInovação57.40
1941952024-06-28Direcção De Recursos HumanosServiços Digitais82.4479.38474.091Email14.6810SimTempo De Espera ElevadoUtilizador VulnerávelMobilidade92.63
1951962024-04-22Gabinete De Atendimento Ao CidadãoPortal Online95.0773.901144.3365Presencial4.215NãoServiço EficienteCidadãoMobilidade85.54
1961972024-11-13Gabinete De Atendimento Ao CidadãoAtendimento Presencial76.1492.23664.7267Chatbot16.068SimMuito SatisfeitoUtilizador VulnerávelInovação95.35
1971982024-02-21Serviços De TiEmail65.3687.361185.0215Portal18.986SimTempo De Espera ElevadoEstudanteDigitalização56.62
1981992024-09-24Departamento De Planeamento EstratégicoChatbot61.1584.94924.586Telefone12.513SimSistema LentoEstudanteSocial79.65
1992002024-10-21Serviços De TiEmail90.2195.031133.8486Portal13.5612SimServiço EficienteCidadãoDigitalização79.96